Near-Optimal BRL using Optimistic Local Transitions

Model-based Bayesian Reinforcement Learning (BRL) allows a found formalization of the problem of acting optimally while facing an unknown environment, i.e., avoiding the exploration-exploitation dilemma. However, algorithms explicitly addressing BRL suffer from such a combinatorial explosion that a large body of work relies on heuristic algorithms. This paper introduces BOLT, a simple and (almost) deterministic heuristic algorithm for BRL which is optimistic about the transition function. We analyze BOLT's sample complexity, and show that under certain parameters, the algorithm is near-optimal in the Bayesian sense with high probability. Then, experimental results highlight the key differences of this method compared to previous work.Comment: ICML201

Accidentes laborales: ¿responsabilidad penal del empresario?

45 p.La investigación que se presenta, procura dilucidar si la regulación positiva, actualmente vigente en nuestro país, permite sustentar la imputación penal del empresario, a raíz de accidentes laborales ocurridos al interior de una empresa. Para responder a tal interrogante, requeriremos abordar con intensidades diversas, el incremento del riesgo que se produce al interior de las diferentes entidades empresariales, como asimismo las características de dirección y organización, propias y diferenciables, que se establecen dentro de las mismas, y en fin, la pertinencia que ostenta el derecho penal para adentrarse en temas enlazados con accidentabilidad laboral. Hecho lo anterior, la respuesta demanda un análisis de la doctrina y legislación comparada, que dan cuenta de soluciones en este asunto. Con esa información, se estudia la legislación nacional vigente, penal y extrapenal, y se adopta una solución, guiado en el análisis por un caso concreto, y real, con el que se busca establecer una regla para situaciones análogas.Palabras claves: Derecho Penal, imputación,empresa, empresario, accidentes laborales. /ABSTRACT:The following investigation seeks to elucidate if the existing positive regulation, currently in vigor in our country, can allow for attribution of criminal liability for the entrepreneur, with regard to company labor accidents. To provide an answer to this issue, it is necessary to deal with risk increase elements within the wide scope of enterprises, and – as well – unique direction and organization traits, and finally, the question of whether it is pertinent for criminal law to regulate issues related to labor accidents. The latter being said, an adequate answer requires a thorough analysis of doctrine and compared legislation that can provide solutions for this matter. With this information, the following paper studies the national legislation in vigor, criminal and non criminal, and adopts a solution, guided by the analysis of a real and concrete case, with which it seeks to establish a rule for analogous situations. Keywords: Criminal Law, liability, entrepreneur, labor accident

Deep Recurrent Architectures for Seismic Tomography

This paper introduces novel deep recurrent neural network architectures for Velocity Model Building (VMB), which is beyond what Araya-Polo et al 2018 pioneered with the Machine Learning-based seismic tomography built with convolutional non-recurrent neural network. Our investigation includes the utilization of basic recurrent neural network (RNN) cells, as well as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells. Performance evaluation reveals that salt bodies are consistently predicted more accurately by GRU and LSTM-based architectures, as compared to non-recurrent architectures. The results take us a step closer to the final goal of a reliable fully Machine Learning-based tomography from pre-stack data, which when achieved will reduce the VMB turnaround from weeks to days.Comment: Published in the 81st EAGE Conference and Exhibition, 201